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return-asymmetry-effect-in-commodity-futures.py
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return-asymmetry-effect-in-commodity-futures.py
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# https://quantpedia.com/strategies/return-asymmetry-effect-in-commodity-futures/
#
# The investment universe consists of 22 commodity futures, namely:
# soybean oil, corn, cocoa, cotton, feeder cattle, gold, copper, heating oil, coffee, live cattle, lean hogs,
# natural gas, oats, orange juice, palladium, platinum, soybean, sugar, silver, soybean meal, wheat, and crude oil.
# Firstly, at the beginning of each month, construct the asymmetry measure (IE) for each commodity based on the latest 260 daily returns using the following formula
# (the formula originally consists of theoretical density and integrals, however the solution is simple when empirical distribution is utilized):
# IE = (number of trading days when the daily return is greater than the average plus two standard deviations) –
# (number of trading days when the daily return is smaller than the average minus two standard deviations).
# Then rank the commodities according to their IE.
# Buy the bottom seven commodities with the lowest IE in the previous month and sell the top seven commodities with the highest IE in the previous month.
# Weigh the portfolio equally and rebalance monthly.
#
# QC Implementation:
# - Universe consists of Quantpedia comodity futures.
# - Buying bottom 7 commodities and selling top 7 commodities according to IE.
from AlgorithmImports import *
class ReturnAsymmetryEffectInCommodityFutures(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)
self.tickers = [
"CME_S1", # Soybean Futures, Continuous Contract
"CME_W1", # Wheat Futures, Continuous Contract
"CME_SM1", # Soybean Meal Futures, Continuous Contract
"CME_BO1", # Soybean Oil Futures, Continuous Contract
"CME_C1", # Corn Futures, Continuous Contract
"CME_O1", # Oats Futures, Continuous Contract
"CME_LC1", # Live Cattle Futures, Continuous Contract
"CME_FC1", # Feeder Cattle Futures, Continuous Contract
"CME_LN1", # Lean Hog Futures, Continuous Contract
"CME_GC1", # Gold Futures, Continuous Contract
"CME_SI1", # Silver Futures, Continuous Contract
"CME_PL1", # Platinum Futures, Continuous Contract
"CME_CL1", # Crude Oil Futures, Continuous Contract
"CME_HG1", # Copper Futures, Continuous Contract
"CME_LB1", # Random Length Lumber Futures, Continuous Contract
# "CME_NG1", # Natural Gas (Henry Hub) Physical Futures, Continuous Contract
"CME_PA1", # Palladium Futures, Continuous Contract
"CME_RR1", # Rough Rice Futures, Continuous Contract
"CME_RB2", # Gasoline Futures, Continuous Contract
"CME_KW2", # Wheat Kansas, Continuous Contract
"ICE_CC1", # Cocoa Futures, Continuous Contract
"ICE_CT1", # Cotton No. 2 Futures, Continuous Contract
"ICE_KC1", # Coffee C Futures, Continuous Contract
"ICE_O1", # Heating Oil Futures, Continuous Contract
"ICE_OJ1", # Orange Juice Futures, Continuous Contract
"ICE_SB1" # Sugar No. 11 Futures, Continuous Contract
"ICE_RS1", # Canola Futures, Continuous Contract
"ICE_GO1", # Gas Oil Futures, Continuous Contract
"ICE_WT1", # WTI Crude Futures, Continuous Contract
]
self.data = {} # storing objects of SymbolData class keyed by comodity symbols
self.period = 261 # need 261 daily prices, to calculate 260 daily returns
self.buy_count = 7 # buy n comodities on each rebalance
self.sell_count = 7 # sell n comodities on each rebalance
self.symbol = self.AddEquity("SPY", Resolution.Daily).Symbol
# subscribe to futures contracts
for ticker in self.tickers:
security = self.AddData(QuantpediaFutures, ticker, Resolution.Daily)
security.SetFeeModel(CustomFeeModel())
security.SetLeverage(5)
self.data[security.Symbol] = SymbolData(self.period)
self.rebalance_flag = False
self.Schedule.On(self.DateRules.MonthStart(self.symbol), self.TimeRules.BeforeMarketClose(self.symbol, 0), self.Rebalance)
def OnData(self, data):
# update daily closes
for symbol in self.data:
if symbol in data and data[symbol]:
close = data[symbol].Value
self.data[symbol].update_closes(close)
# rebalance monthly
if not self.rebalance_flag:
return
self.rebalance_flag = False
IE = {}
for symbol, symbol_obj in self.data.items():
# check if comodity has ready prices
if not symbol_obj.is_ready():
continue
# calculate IE
IE_value = symbol_obj.calculate_IE()
# store IE value under comodity symbol
IE[symbol] = IE_value
# make sure, there are enough comodities for rebalance
if len(IE) < (self.buy_count + self.sell_count):
return
# sort commodities based on IE values
sorted_by_IE = [x[0] for x in sorted(IE.items(), key=lambda item: item[1])]
# select long and short parts
long = sorted_by_IE[:self.buy_count]
short = sorted_by_IE[-self.sell_count:]
# trade execution
invested = [x.Key for x in self.Portfolio if x.Value.Invested]
for symbol in invested:
if symbol not in long + short:
self.Liquidate(symbol)
for symbol in long:
self.SetHoldings(symbol, 1 / self.buy_count)
for symbol in short:
self.SetHoldings(symbol, -1 / self.sell_count)
def Rebalance(self):
self.rebalance_flag = True
class SymbolData():
def __init__(self, period):
self.closes = RollingWindow[float](period)
def update_closes(self, close):
self.closes.Add(close)
def is_ready(self):
return self.closes.IsReady
def calculate_IE(self):
closes = np.array([x for x in self.closes])
daily_returns = (closes[:-1] - closes[1:]) / closes[1:]
average_daily_returns = np.average(daily_returns)
two_daily_returns_std = 2 * np.std(daily_returns)
avg_plus_two_std = average_daily_returns + two_daily_returns_std
avg_minus_two_std = average_daily_returns - two_daily_returns_std
over_avg_plus_two_std = 0 # counting number of daily returns, which were over avg_plus_two_std
under_avg_minus_two_std = 0 # counting number of daily returns, which were under avg_minus_two_std
for daily_return in daily_returns:
if daily_return > avg_plus_two_std:
over_avg_plus_two_std += 1
elif daily_return < avg_minus_two_std:
under_avg_minus_two_std += 1
IE_value = over_avg_plus_two_std - under_avg_minus_two_std
return IE_value
# Quantpedia data.
# NOTE: IMPORTANT: Data order must be ascending (datewise)
class QuantpediaFutures(PythonData):
def GetSource(self, config, date, isLiveMode):
return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)
def Reader(self, config, line, date, isLiveMode):
data = QuantpediaFutures()
data.Symbol = config.Symbol
if not line[0].isdigit(): return None
split = line.split(';')
data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1)
data['back_adjusted'] = float(split[1])
data['spliced'] = float(split[2])
data.Value = float(split[1])
return data
# Custom fee model.
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))